OpenClaw

df-merger

Merge pandas DataFrames from multiple construction sources. Handle different schemas, keys, and data quality issues.

datadrivenconstruction0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install datadrivenconstruction/df-merger

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

DataFrame Merger for Construction Data

Overview

Construction projects combine data from BIM, schedules, costs, and sensors. This skill merges DataFrames from disparate sources with intelligent key matching and schema reconciliation.

Python Implementation

import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
from difflib import SequenceMatcher


class MergeStrategy(Enum):
    """DataFrame merge strategies."""
    INNER = "inner"       # Only matching rows
    LEFT = "left"         # All left, matching right
    RIGHT = "right"       # Matching left, all right
    OUTER = "outer"       # All rows from both
    CROSS = "cross"       # Cartesian product


@dataclass
class MergeResult:
    """Result of merge operation."""
    merged_df: pd.DataFrame
    matched_rows: int
    left_only: int
    right_only: int
    merge_quality: float  # 0-1 score


class ConstructionDFMerger:
    """Merge DataFrames from construction sources."""

    # Common construction column name mappings
    COLUMN_MAPPINGS = {
        'element_id': ['elementid', 'elem_id', 'id', 'guid', 'globalid'],
        'type_name': ['typename', 'type', 'element_type', 'category'],
        'level': ['level', 'floor', 'storey', 'building_storey'],
        'material': ['material', 'mat', 'material_name'],
        'volume': ['volume', 'vol', 'volume_m3', 'qty_volume'],
        'area': ['area', 'surface_area', 'qty_area', 'area_m2'],
        'cost': ['cost', 'price', 'total_cost', 'amount'],
        'task_id': ['task_id', 'activity_id', 'wbs', 'activity'],
        'start_date': ['start', 'start_date', 'planned_start', 'begin'],
        'end_date': ['end', 'end_date', 'planned_finish', 'finish']
    }

    def __init__(self):
        self.column_cache: Dict[str, str] = {}

    def find_common_key(self, df1: pd.DataFrame,
                        df2:...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/datadrivenconstruction/df-merger
Version: —
Commit: 649f984c9d45bd523edf9416a6dceda3a1c56c84
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# DataFrame Merger for Construction Data

## Overview
Construction projects combine data from BIM, schedules, costs, and sensors. This skill merges DataFrames from disparate sources with intelligent key matching and schema reconciliation.

## Python Implementation

```python
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
from difflib import SequenceMatcher


class MergeStrategy(Enum):
    """DataFrame merge strategies."""
    INNER = "inner"       # Only matching rows
    LEFT = "left"         # All left, matching right
    RIGHT = "right"       # Matching left, all right
    OUTER = "outer"       # All rows from both
    CROSS = "cross"       # Cartesian product


@dataclass
class MergeResult:
    """Result of merge operation."""
    merged_df: pd.DataFrame
    matched_rows: int
    left_only: int
    right_only: int
    merge_quality: float  # 0-1 score


class ConstructionDFMerger:
    """Merge DataFrames from construction sources."""

    # Common construction column name mappings
    COLUMN_MAPPINGS = {
        'element_id': ['elementid', 'elem_id', 'id', 'guid', 'globalid'],
        'type_name': ['typename', 'type', 'element_type', 'category'],
        'level': ['level', 'floor', 'storey', 'building_storey'],
        'material': ['material', 'mat', 'material_name'],
        'volume': ['volume', 'vol', 'volume_m3', 'qty_volume'],
        'area': ['area', 'surface_area', 'qty_area', 'area_m2'],
        'cost': ['cost', 'price', 'total_cost', 'amount'],
        'task_id': ['task_id', 'activity_id', 'wbs', 'activity'],
        'start_date': ['start', 'start_date', 'planned_start', 'begin'],
        'end_date': ['end', 'end_date', 'planned_finish', 'finish']
    }

    def __init__(self):
        self.column_cache: Dict[str, str] = {}

    def find_common_key(self, df1: pd.DataFrame,
                        df2:...

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